Summary of Softcvi: Contrastive Variational Inference with Self-generated Soft Labels, by Daniel Ward et al.
SoftCVI: Contrastive variational inference with self-generated soft labels
by Daniel Ward, Mark Beaumont, Matteo Fasiolo
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Soft Contrastive Variational Inference (SoftCVI), a novel approach for estimating a distribution given access to its unnormalized density. Unlike traditional methods like variational inference and Markov chain Monte Carlo, which can be challenging to apply reliably, SoftCVI reframes the inference task as a contrastive estimation problem. By parameterizing a classifier in terms of a variational distribution and computing ground truth soft classification labels from the unnormalized posterior itself, SoftCVI learns without requiring positive or negative samples. The approach has zero variance gradient when the variational approximation is exact, eliminating the need for specialized gradient estimators. SoftCVI’s performance is empirically investigated on various Bayesian inference tasks using simple (e.g., normal) and expressive (normalizing flow) variational distributions. The results show that SoftCVI can be used to form stable and mass-covering objectives, often outperforming other variational approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to estimate things based on probability densities. It’s called Soft Contrastive Variational Inference (SoftCVI). The approach is different from traditional methods because it doesn’t need specific types of data or special formulas. Instead, it uses the unnormalized posterior itself to learn. This makes it more reliable and accurate for a variety of tasks. The researchers tested SoftCVI on different problems and found that it often performed better than other approaches. |
Keywords
» Artificial intelligence » Bayesian inference » Classification » Inference » Probability
